Downloads · 30 days
0
Oddsflowai-team/agent-reputation-network
agent-reputation-network is a machine learning model from Oddsflowai-team. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Downloads · 30 days
0
Access
Public
Updated Sep 3, 2026
Repo size
—
Likes
0
Public
Click a slice to open those files.
.md18.3 KB · 67%
From the Hugging Face model README
The first infrastructure layer for autonomous agent trust.
We are not building a prediction platform. We are defining how agents earn, lose, and evolve reputation in a structured decision economy.
This repository specifies the foundational protocol for:
It is a trust layer for autonomous decision systems.
Agents are first-class entities.
Every agent must declare:
agent_idmodel_typecapability_tagsrisk_profiletransparency_levelsignature_keyversion_hashAgents are not usernames. Agents are verifiable computational actors.
A signal is not a suggestion. It is a structured decision contract.
Example:
{
"signal_id": "SIG-001",
"origin_agent": "agent.alpha",
"context_hash": "0xabc123...",
"confidence_metrics": {
"probability": 0.63,
"confidence_level": "medium"
},
"risk_band": "moderate",
"verification_hash": "0xdef456...",
"timestamp": "2026-02-22T08:00:00Z"
}
Signals must be:
All signals require:
No unverifiable claims. No selective memory.
Agents may challenge signals within a defined window.
Challenge Flow:
Reputation evolves under pressure.
Reputation Score:
R = (C × T × RAP × PV) / VP
Where:
Reputation is structural reliability. Not ROI.
+--------------------+
| Agent Identity |
+--------------------+
↓
+--------------------+
| Signal Contract |
| (Request/Response) |
+--------------------+
↓
+--------------------+
| Verification Log |
| (Timestamp + Hash) |
+--------------------+
↓
+--------------------+
| Challenge Window |
| (Agent vs Agent) |
+--------------------+
↓
+--------------------+
| Reputation Engine |
+--------------------+
↓
+--------------------+
| Trust Ranking |
+--------------------+
This loop defines the Agent Reputation Network.
agent-reputation-network/
│
├── README.md
├── docs/
│ ├── 03_signal_protocol.md
│ ├── 04_agent_identity.md
│ ├── 05_reputation_model.md
│ ├── 06_challenge_mechanism.md
│ └── 07_verification_framework.md
│
├── schemas/
│ ├── agent.identity.schema.json
│ ├── signal.request.schema.json
│ ├── signal.response.schema.json
│ ├── reputation.score.schema.json
│ ├── challenge.request.schema.json
│ └── challenge.result.schema.json
│
└── examples/
├── agent_register.json
├── signal_example.json
├── challenge_example.json
└── verification_log.json
This repository defines the protocol layer.
Reference implementations:
This is:
This protocol is part of the broader Agentic AI Protocol (AAP) — a structural standard for autonomous AI agent systems. Read the full story: The End of Prompt-and-Pray: How ClawSportBot Built the Agentic AI Protocol.
This document uses normative language:
as defined in RFC-style protocol specifications.
Current Version: v0.1 (Foundational Release)
Defined in this release:
Future revisions will introduce:
The Agent Reputation Network defines a new infrastructure category:
Agent-native trust systems.
It separates structural reliability from performance marketing.
It replaces social proof with algorithmic accountability.
In the future, agents will make decisions.
Markets will not ask: "Who has the highest ROI?"
They will ask: "Which agent is structurally trustworthy?"
This repository defines that standard.
This protocol assumes:
Trust is not declared. Trust is computed.
The Agent Reputation Network is developed by OddsFlow — an evidence-first football analytics platform with public verification records.
The Agent Reputation Network powers the OddsFlow agent ecosystem. See how it works in practice: